Intermetallic NiMn-based Heusler alloys (HAs) have attracted great consideration owing to their multi-functionality and exploration in various fields covering sensors, actuation, refrigeration, and waste heat harvesters. Among the NiMn-based HAs, Ni-Mn-Sn alloys have gained much attention since the structural and magnetic transformation was discovered. Many studies have been conducted with different compositions and shapes to explore the physical properties of Ni-Mn-Sn HAs as they present many advantages, such as being non-toxic, low-cost, and having abundant constituents. The Co-doping effect on the physical properties of Ni-Mn-Sn alloys has been widely reported. This doping can rectify the ternary Ni-Mn-Sn Heusler compound's brittleness by crystallizing a disordered face-centered cubic (fcc) γ-phase.

In this study, a polycrystalline Ni₄₃Mn₄₅CoSn₁₁ Heusler alloy was prepared by high-frequency fusion (HF), using a Lin Therm 600 device, from pure Ni, Mn, Sn, and Co elements with appropriate proportions. X−ray diffractometer (BRUCKER D8 Discover), scanning electron microscope (FEI Quantum 250), and magnetometer BS1 devices were used to study the structural, microstructural, and magnetic properties.

The XRD results revealed the coexistence of an ordered L2₁ cubic-austenite phase (~88%) and a disordered cubic solid solution γ-phase (~12%). The alloy undergoes a second-order ferromagnetic to paramagnetic phase transition at a Curie temperature of Tc = 350 K. Landau and Hamad's theoretical models have been used to re-plot the magnetic entropy change. The magnetocaloric properties (the maximum entropy change value, ΔS_M, the full width at half maximum of the entropy change curve, δT_FWHM, the relative cooling power, RCP, and the heat capacity, ΔC_P,H ) have been calculated using the isothermal magnetization curves with the phenomenological model of Hamad.

Currently, artificial neural networks (ANNs) have become a powerful tool for tackling a wide range of complex problems in machine learning, with exceptional capabilities in areas such as image recognition, natural language processing, and autonomous systems [1]. However, while the theoretical advancements of ANNs have been substantial, translating these models into efficient and scalable hardware remains a challenging task. Conventional digital computing systems, which are predominantly based on CMOS technologies, are limited by their high energy consumption. One alternative approach is wave-based computing, where information processing is achieved through the propagation and interference of waves within a physical medium. In these systems, the wave interference patterns can naturally facilitate complex, all-to-all connectivity, which is a fundamental requirement for dense and adaptive neural architectures [2].

In this study, we investigate the use of spin waves as a novel platform for implementing neural network functionalities. Spin waves are collective oscillations of electron spins in magnetic materials, operating at microwave frequencies with remarkably short wavelengths, making them ideal for developing high-speed signal processing devices. Based on the micromagnetic simulations, we propose a novel spin wave-based architecture, where spin waves serve in the dynamical coupling of magnetic molecules, which are deposited on the low-damping magnetic substrate, specifically yttrium iron garnet (YIG). By analyzing the dispersion relations and transmission spectra of spin waves in YIG, we confirm the feasibility of establishing dynamic interconnections between these molecular nodes. This mechanism can enable magnetic molecules to function as artificial neurons, interconnected via propagating spin waves. Therefore, such a framework can pave the way for new directions in molecular-based neuromorphic computing.

[1] Marković, D., Mizrahi, A., Querlioz, D., & Grollier, J. (2020). Physics for neuromorphic computing. Nature Reviews Physics, 2(9), 499-510.
[2] Papp, Á., Porod, W., & Csaba, G. (2021). Nanoscale neural network using non-linear spin-wave interference. Nature communications, 12(1), 6422.

This work presents a predictive model for nanofiber diameters based on solution and process parameters in the electrospinning technique. The study emphasizes polymer–solvent interactions, characterized through cohesive energy parameters such as Hansen, Hildebrand, and Flory–Huggins, which describe molecular compatibility. These parameters were used to train seven Generalized Linear Models (GLMs) across three continuous distribution families: Gaussian, Gamma, and Inverse Gaussian—each fitted with identity, log, or reciprocal link functions.

Model performance was evaluated using Akaike (AIC) and Bayesian (BIC) information criteria, deviance, and AIC weights. These metrics guided the selection of individual models and supported the construction of a mean AIC-weighted model, optimizing both accuracy and parsimony. While several models exhibited robust predictive capabilities, the AIC-weighted average model provided slightly enhanced performance, aligning with previous findings that model averaging can reduce selection bias and improve generalization. Among the top-performing models, the Gaussian distribution with a log-link function demonstrated strong goodness-of-fit and parameter stability.

The dataset comprised electrospinning conditions for various natural and synthetic polymers, including cellulose acetate (AC), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), polycaprolactone (PCL), polyvinyl chloride (PVC), and polymethyl methacrylate (PMMA). Polymer–solvent compatibility was assessed by calculating Flory–Huggins interaction parameters and Relative Energy Difference (RED) values via Hansen solubility distance (Ra), compared against the interaction radius (R₀).

Key statistical indicators such as standardized deviance residuals, coefficient significance, and Pearson correlation were used to identify the most influential predictors. The Flory–Huggins parameter and Hansen solubility components emerged as the most significant, followed by process variables such as needle diameter and solution flow rate. Experimental validation with electrospun PVA fibers confirmed the model’s reliability for guiding nanoscale fiber design.

Hybrid organic–inorganic perovskites, particularly methylammonium lead iodide (MAPbI₃), have attracted considerable interest due to their exceptional photoelectric properties, making them promising candidates for next-generation solar cells [1,2]. However, despite these advantages, MAPbI₃-based perovskites are susceptible to ion migration, during which dissociation into methylammonium (MA⁺) and iodide (I⁻) ions occurs [3]. This phenomenon leads to instability in device performance, including the emergence of hysteresis in current–voltage (J–V) characteristics.

In this study, drift-diffusion modeling is employed to analyze these effects. This approach enables a detailed investigation of hysteresis behavior and allows for the assessment of how scan rate, carrier lifetime, and charge carrier mobility influence the shape of the J–V curves in perovskite solar cells [4].

The mobilities of cations and anions are equal (μₐ = μ_c). At high mobility (10⁻⁸ cm²/V·s), the J–V curve appears symmetric with minimal hysteresis, indicating rapid ion redistribution and efficient compensation of the internal electric field. In contrast, the results show in which cation mobility is fixed at 10⁻¹² cm²/V·s, while anion mobility varies from 10⁻⁸ to 10⁻¹² cm²/V·s. As anion mobility decreases, hysteresis progressively intensifies: at the lowest value (μₐ = 10⁻¹² cm²/V·s), the reverse J–V curve exhibits a pronounced shift, indicating significant distortion in charge transport processes.

This work was supported by Grant No. AP27508227 of the Ministry of Science and Higher Education of the Republic of Kazakhstan.

Methylammonium tin iodide (CH₃NH₃SnI₃) has attracted significant attention in recent years as a promising lead-free material for perovskite solar cells (PSCs), offering an environmentally friendly alternative to traditional lead-based compounds. With a direct band gap of approximately 1.3 eV, high hole mobility, and favorable charge transport properties, CH₃NH₃SnI₃ possesses strong theoretical potential for high-efficiency solar energy conversion [1-3].

However, further development of devices based on this material is limited by several fundamental challenges, most notably the pronounced hysteresis in current–voltage (J–V) characteristics. This behavior is associated with slow internal dynamic processes, including ion migration and interfacial charge relaxation [4].

In this study, numerical simulations based on the drift-diffusion model were conducted to investigate the role of ion-mediated recombination and ionic mobility in the formation of hysteresis in CH₃NH₃SnI₃-based devices. Particular attention was paid to the influence of carrier lifetime (τ), a key parameter governing the efficiency of photogenerated carrier generation, transport, and extraction. The simulation results show that increasing τ significantly reduces bulk and interfacial recombination losses, minimizes the hysteresis index (HI), enhances the short-circuit current density (J_sc = 31.62 mA/cm²), and improves the operational stability of the device under both forward and reverse scan directions. Analysis of the J–V characteristics as a function of carrier lifetime confirmed that longer lifetimes lead to improved photovoltaic performance, reduced impact of ion migration, and enhanced output stability. Thus, this study highlights the critical importance of precise control over carrier lifetime and ionic mobility in improving the efficiency and long-term stability of CH₃NH₃SnI₃-based PSCs. It also demonstrates the potential of numerical modeling as an effective tool for the engineering optimization of perovskite photovoltaic devices.

This work was supported by the Grant No. AP19174728 of the Ministry of Science and Higher Education of the Republic of Kazakhstan.

In this study, molecular dynamics (MD) simulations were conducted to thoroughly investigate the mechanical properties of poly(vinylidene fluoride) (PVDF), both in its pure form and when reinforced with barium titanate (BTO) nanoparticles, using the advanced Materials Studio software package. After performing detailed MD optimization and the subsequent in-depth analysis, the stiffness matrix along with key mechanical parameters—including the Young’s modulus, shear modulus, and bulk modulus—were precisely calculated for each system. Four different configurations were carefully examined to assess the influence of varying BTO contents on the composite’s overall mechanical behavior: PVDF-0, representing neat PVDF without any reinforcement; PVDF-I, containing 7.57 wt% BTO; PVDF-II, with 14.07 wt% BTO; and PVDF-III, with 19.72 wt% BTO. The comparative analysis of these configurations revealed that even a low weight fraction of BTO nanoparticles leads to a significant improvement in the mechanical performance of PVDF, particularly in terms of its stiffness and resistance to deformation under various mechanical loading conditions and environmental factors. These important findings clearly demonstrate the promising potential of BTO as an effective reinforcing agent to enhance the mechanical properties of PVDF-based nanocomposites, thus opening new avenues for their practical application in advanced materials science and engineering fields where enhanced mechanical and functional properties are critically desired.

In this contribution, a refined two-dimensional theory, based on higher-order kinematic models and the equivalent single-layer approach, is presented for the static and dynamic analysis of laminated doubly curved shell structures made of advanced materials and subjected to arbitrary loading and boundary conditions. Each layer of the structure is made of advanced materials, including short- and long-fiber-reinforced composites, lattice and honeycomb cores, and functionally graded materials, among others. Furthermore, the effect of the reinforcement with agglomerated carbon nanotubes (CNTs) is studied, with the material properties homogenized through an analytical procedure based on the Mori–Tanaka approach. The fundamental equations are derived in curvilinear principal coordinates from the Hamilton principle, expressed in both strong and weak forms. A numerical solution is obtained using the Generalized Differential and Integral Quadrature methods. In the post-processing step, an efficient recovery procedure reconstructs the three-dimensional (3D) response of the structure by means of the equilibrium equations. In addition, mode frequencies and shapes are successfully evaluated for dynamic analysis. The model is systematically validated against 3D solutions from finite-element-based commercial software for both mode frequencies and stress distribution. Then, parametric investigations are conducted through various numerical examples, pointing out the sensitivity of the model to geometric and material parameters, including agglomeration parameters, volume fractions of constituent materials, kinematic models, and curvature effects, among others. The proposed approach is demonstrated to be more computationally efficient than classical numerical methods and allows a straightforward modification of the governing parameters in each simulation. The results of the analysis offer a useful tool for studying the static and dynamic behavior of these structures, thus allowing us to adopt advanced structural components in the design process. In this way, new possibilities can arise for a broader application of these structures and materials.

This research investigates the magnetoresistance (MR) behavior of Co/Cu magnetic superlattices with a fixed cobalt (Co) layer thickness of 20 Å, focusing on how variations in the copper (Cu) layer's thickness affect the MR response across a broad temperature range (4.2–300 K). This study specifically explores the influence of the Cu layer's thickness, interfacial structure, and surface morphology on spin-dependent electron scattering, which is the dominant mechanism governing the MR ratio in such multilayered systems. A detailed theoretical framework is employed, incorporating spin-dependent Boltzmann transport equations and realistic interface models, to capture how modifications in the electron reflection, transmission, and spin filtering at the Co/Cu interfaces influence MR. Numerical simulations reveal a pronounced decrease in the MR as the Cu's thickness increases from 5 Å to 150 Å, particularly at cryogenic temperatures, where ballistic and quantum transport effects are more pronounced. This trend is attributed to reduced spin asymmetry and enhanced diffuse scattering in thicker Cu layers. The theoretical results show excellent agreement with experimental measurements conducted on electrodeposited multilayers, demonstrating the validity of the model. These findings highlight the critical importance of controlling both the Cu spacer's thickness and the interface integrity to optimizing the MR performance. These insights are essential for the design of advanced spintronic devices, where precise engineering at the atomic scale is required to achieve high spin polarization and efficient electron transport.

Janus nanoparticles (JPs), consisting of hemispherical metal–dielectric interfaces, exhibit structural asymmetry that enables polarization-sensitive localized surface plasmon resonances (LSPRs). Unlike isotropic nanoparticles, JPs enable spatially confined near-field enhancement at the material interface, where one hemisphere concentrates the field and the other can be selectively functionalized for targeted sensing applications. We used finite-difference time-domain (FDTD) simulations to study the optical and near-field responses of Au–SiO₂ JPs across diameters ranging from 10 to 100 nm. Four distinct illumination configurations were modeled, each defined by a specific incident polarization relative to the Janus interface, covering both longitudinal and transverse field orientations. We calculated absorption, scattering, and extinction spectra, and evaluated electric field enhancement (|E/E₀|²) spectrally and spatially within the cross-sectional plane normal to the propagation direction. Simulations reveal that when the electric field is aligned parallel to the interface, the resulting near-field is localized at the metal–dielectric boundary and exhibits approximately a 3.4x stronger E-field enhancement compared to the perpendicular configuration. Across all polarization setups, we consistently observe that the maximum near-field intensity occurs at the particle size where absorption and scattering cross-sections intersect—a resonance crossover point that defines an optimal condition for field enhancement. Notably, the parallel polarization configurations produce up to a 18.7x greater E-field enhancement than the perpendicular mode at their respective optimal sizes. Compared to spherical Au nanoparticles, Au-SiO₂ JPs exhibit redshifted LSPR peaks, increased E-field enhancement, and polarization-dependent field enhancement at the metal–dielectric interface. This study demonstrates that JPs enable interface-localized field confinement, with polarization and size serving as tunable parameters for optimizing hotspot activation and magnitude. These findings establish a framework for designing asymmetric plasmonic nanostructures which enable spatial and spectral control of near-field enhancement properties—paving the way for high-performance, targeted nanosensors.

This work presents a homogenization strategy to analyze honeycomb sandwich shell structures, employing the Representative Volume Element (RVE) method to extract equivalent orthotropic properties of periodic lattice cores with varying geometries. These properties are integrated into homogenized models of infinite panels using double periodic boundary conditions to assess vibrational behavior under different loading and geometric conditions.

Recent advancements in nanomaterials have enabled the development of high-performance structural components. Nanostructures, due to their nanoscale features, offer superior mechanical and thermal properties, enhancing stiffness-to-weight ratios, damping, and energy absorption. When embedded in lattice or sandwich architectures, they become ideal for aerospace and automotive applications. Homogenization plays a crucial role in integrating such materials into periodic designs while maintaining predictive accuracy.

A key advantage of the proposed methodology is its flexibility in material selection. The study examines different base materials—including aluminum, steel, and AlSiC—without the need for repeated 3D meshing. This enables efficient parametric analyses, reducing computational cost and time while preserving accuracy.

Validation is performed by comparing natural frequencies from full 3D finite element models with those from the homogenized simulations. The results show excellent agreement, with discrepancies generally under 5%, confirming the method’s reliability.

Additionally, the influence of curvature—both cylindrical and spherical—is systematically analyzed to evaluate its impact on modal behavior. The proposed framework offers a robust, computationally efficient tool for the design and optimization of lightweight, multi-material structures. Its application is highly relevant in fields where weight reduction, structural integrity, and design adaptability are critical, such as aerospace, automotive, and civil engineering.